在比较市民和市议员的意见时,利用政治问题的相关性属性进行立场预测

IF 1.6 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE International Journal on Digital Libraries Pub Date : 2024-02-26 DOI:10.1007/s00799-024-00396-3
Ko Senoo, Yohei Seki, Wakako Kashino, Atsushi Keyaki, Noriko Kando
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引用次数: 0

摘要

本研究的重点是区分市民和市议员在政治问题上的立场(即赞成或反对)的方法,并尝试比较双方的论点。我们创建了一个数据集,在市民推文和市议会会议记录上标注了四个属性:立场、有用性、区域依赖性和相关性。然后,我们使用该数据集对预训练的大型语言模型进行了微调,以自动为大量未标注数据分配属性标签。我们引入了多任务学习,对每个属性和相关性进行联合训练,以便通过关注与政治问题相关的句子来识别线索。我们的预测模型基于 T5,这是一个适合多任务学习的大型语言模型。我们将我们的系统与使用 BERT 或 RoBERTa 的系统的结果进行了比较。实验结果表明,通过多任务学习,公民推文的宏观立场 F1 分数提高了 1.8%,市议会会议记录的宏观立场 F1 分数提高了 1.7%。通过使用微调模型分析真实的意见差距,我们发现虽然福冈市的市议员对疫苗接种制度给予了积极评价,但市民对其评价并不高。
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Stance prediction with a relevance attribute to political issues in comparing the opinions of citizens and city councilors

This study focuses on a method for differentiating between the stance of citizens and city councilors on political issues (i.e., in favor or against) and attempts to compare the arguments of both sides. We created a dataset by annotating citizen tweets and city council minutes with labels for four attributes: stance, usefulness, regional dependence, and relevance. We then fine-tuned pretrained large language model using this dataset to assign the attribute labels to a large quantity of unlabeled data automatically. We introduced multitask learning to train each attribute jointly with relevance to identify the clues by focusing on those sentences that were relevant to the political issues. Our prediction models are based on T5, a large language model suitable for multitask learning. We compared the results from our system with those that used BERT or RoBERTa. Our experimental results showed that the macro-F1-scores for stance were improved by 1.8% for citizen tweets and 1.7% for city council minutes with multitask learning. Using the fine-tuned model to analyze real opinion gaps, we found that although the vaccination regime was positively evaluated by city councilors in Fukuoka city, it was not rated very highly by citizens.

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来源期刊
CiteScore
4.30
自引率
6.70%
发文量
20
期刊介绍: The International Journal on Digital Libraries (IJDL) examines the theory and practice of acquisition definition organization management preservation and dissemination of digital information via global networking. It covers all aspects of digital libraries (DLs) from large-scale heterogeneous data and information management & access to linking and connectivity to security privacy and policies to its application use and evaluation.The scope of IJDL includes but is not limited to: The FAIR principle and the digital libraries infrastructure Findable: Information access and retrieval; semantic search; data and information exploration; information navigation; smart indexing and searching; resource discovery Accessible: visualization and digital collections; user interfaces; interfaces for handicapped users; HCI and UX in DLs; Security and privacy in DLs; multimodal access Interoperable: metadata (definition management curation integration); syntactic and semantic interoperability; linked data Reusable: reproducibility; Open Science; sustainability profitability repeatability of research results; confidentiality and privacy issues in DLs Digital Library Architectures including heterogeneous and dynamic data management; data and repositories Acquisition of digital information: authoring environments for digital objects; digitization of traditional content Digital Archiving and Preservation Digital Preservation and curation Digital archiving Web Archiving Archiving and preservation Strategies AI for Digital Libraries Machine Learning for DLs Data Mining in DLs NLP for DLs Applications of Digital Libraries Digital Humanities Open Data and their reuse Scholarly DLs (incl. bibliometrics altmetrics) Epigraphy and Paleography Digital Museums Future trends in Digital Libraries Definition of DLs in a ubiquitous digital library world Datafication of digital collections Interaction and user experience (UX) in DLs Information visualization Collection understanding Privacy and security Multimodal user interfaces Accessibility (or "Access for users with disabilities") UX studies
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